In July 1848, L'illustration, a French weekly, printed the first photo to appear alongside a story. It depicted Parisian barricades set up during the city's June Days uprising. Nearly two centuries later, photojournalism has bestowed libraries with legions of archival pictures that tell stories of our past. But without a methodical approach to curate them, these historical images could get lost in endless mounds of data. That's why the Library of Congress in Washington, D.C. is undergoing an experiment. Researchers are using specialized algorithms to extract historic images from newspapers.

A fundamental goal of artificial intelligence research and development is the creation of machines that demonstrate what humans consider to be intelligent behavior. Effective knowledge representation and reasoning (KR&R) methods are a foundational requirement for intelligent machines. The development of these methods remains a rich and active area of artificial intelligence research in which advances have been motivated by many factors, including interest in new challenge problems, interest in more complex domains, shortcomings of current methods, improved computational support, increases in requirements to interact effectively with humans, and ongoing funding from Defense Advanced Research Projects Agency and other agencies. The article by Richard Fikes and Tom Garvey, Knowledge Representation and Reasoning – A History of DARPA Leadership, highlights several decades of advances in KR&R, paying particular attention to research on planning and on the impact of DARPA's support. Fikes and Garvey are joined by David Israel, a principal scientist in the Artificial Intelligence Center at SRI International, who provides his own brief commentary on KR&R.

HANSCOM AIR FORCE BASE, Mass. Two members of the Digital Directorate's Command, Control, Intelligence, Surveillance and Reconnaissance division at Robins Air Force Base, Georgia, collaborated with personnel from the Air Force Research Laboratory and Air Combat Command to see if real time analytics, or RTA, could act as an assistant to DCGS analysts. "Operational analysts have so much data to look at, and it's coming at them fast and furious," said Dr. Chris "Jake" Jacobson, solutions architect and product owner from ACC. "We wanted to see if we could use RTA to help with that overwhelming amount of data streaming at the analysts." RTA is an open, modular IT platform that acts as a harness to host AI and ML algorithms. Users identified full motion video, or FMV, as an initial area where they thought this could provide benefits.

Turns out that AI is not able to be a patent holder, plus other thorny topics. Can AI be an inventor? According to a recent decision by the U.S. Patent and Trademark Office (USPTO), the answer seems to be no. There is more to this story, though, and we'll need to push past the surface to understand the full nuances involved. Perhaps a more apt way to depict the situation is whether AI can be formally granted a U.S. patent, and for that the answer appears to unequivocally and emphatically be a razor-sharp no.

Early in 2018, the volcano Anak Krakatau in Indonesia started falling apart. It was a subtle transformation -- one that nobody noticed at the time. The southern and southwestern flanks of the volcano were slipping towards the ocean at a rate of about 4 millimetres per month, a shift so small that researchers only saw it after the fact as they combed through satellite radar data. By June, though, the mountain began showing obvious signs of unrest. It spewed fiery ash and rocks into the sky in a series of small eruptions. And it was heating up.

We take a closer look at the robotics giant, Nuro. The company believes that great technology should benefit everyone. The team at Nuro is accelerating a future where robots make life easier and help us connect to the people and things we love. Together, they're pushing the boundaries of robotics to improve human life. Dave Ferguson and Jiajun Zhu have devoted their careers to robotics and machine learning, most recently as Principal Engineers at Google's self-driving car project (now Waymo).

Perceiving the pandemics' hard reset as a chance to grow stronger, more resilient, and resourceful dominates manufacturers' mindsets who continue to double down on analytics and AI-driven pilots. Combining human experience, insight, and AI techniques, they're discovering new ways to differentiate themselves while driving down costs and protecting margins. And they're all up for the challenge of continuing to grow in tough economic times. Boston Consulting Group's recent study The Rise of the AI-Powered Company in the Postcrisis World found that in the four previous global economic downturns, 14% of companies were able to increase both sales growth and profit margins as the following graphic shows: AI Is Core To Manufacturing's Real-Time Future Real-time monitoring provides many benefits, including troubleshooting production bottlenecks, tracking scrap rates, meeting customer delivery dates, and more. It's an excellent source of contextually relevant data that can be used for training machine learning models.

We are using machine learning to control situations where there are a lot of variables. Data democratization means everyone has access to these data and tools. There are a ton of great tools out there that help folks who maybe aren't data scientists, but are data science-y and make better decisions at work. The growth of artificial intelligence and machine learning over the last few years is unmistakable. Agencies have realized the potential and real benefits of using the advanced technologies to improve decision making, analyze large databases and address mission challenges.

Over the last several years, the use of artificial intelligence (AI) and machine learning (ML) has maintained consistent growth among businesses. During our 2017 survey of IT decision makers in the United States and Japan, we discovered that approximately 74% of businesses in both regions were already using some form of AI or ML to protect their organizations from cyber threats. When we checked in with both regions at the end of 2018, 73% of respondents we surveyed reported they planned to use even more AI/ML tools in the following year. For this report, we surveyed 800 IT professionals with cybersecurity decision-making power across the US, UK, Japan, and Australia/New Zealand regions at the end of 2019, and discovered that 96% of respondents now use AI/ML tools in their cybersecurity programs. Despite the increase in adoption rates for these technologies, more than half of IT decision makers admitted they do not fully understand the benefits of these tools.

Artificial intelligence is a scientific field that is responsible for finding solutions to complex problems that humans do not have. Machine learning could be used to bypass and dismantle cyber-security systems faster than most prevention and detection tools can keep up. AI will exacerbate existing threats and create new ones, but its speed could prove a great boon for cybercriminals, as it is much more effective at fighting them than human experts. The algorithm is attempted to model a decision mechanism that resembles real human decision mechanisms but is modeled by algorithms. In the context of cybersecurity, artificial intelligence (AI) tries to defend the system by weighing patterns of behavior that indicate a threat against predictive logic.